Rural residents’ perception is an important way of evaluating rural ecosystem services. Different topographies affect the level of ecosystem services, thus affecting residents’ perceived preferences. This study conducted a questionnaire survey of 1176 rural residents in mountainous, hilly, and plain areas of Shandong Province. It analyzed respondents’ satisfaction with ecosystem services and landscape preferences in different topographical areas. The results showed that the perception of ecosystem services was higher than average in all three topographic areas. The perceptions from high to low were cultural services, provisioning services, and regulating services. The perception of ecosystem services was significantly affected by topography, with significant differences between mountainous and plain areas in particular. Rural residents’ perceptions of cultural services varied widely and there were diverse preferences. This study believes that the important concerns in enhancing the perception of ecosystem services among rural residents are to raise villagers’ ecological awareness by meeting their growing spiritual needs and the high sensitivity of older people.

The urban green space ratio (GSR) is the ratio of the urban greening land area to the built-up area and is an indicator for controlling land use for greening in urban development in China. However, the statistical process for the urban GSR by field manual survey or remote sensing imagery visual interpretation is a time-consuming and labor-intensive task. While estimating the urban green canopy ratio (GCR) by spatial information technology has been wildly accepted in the industry. This research investigates the influencing factors between the urban greening land area and the urban green canopy area for exploring the quantitative relationship between GSR and GCR. The results showed that the urban street tree greenbelt is a positive correlation factor, while pavement and water bodies in an urban park are the negative correlation factors. Then, a calculation model of the urban GSR was proposed that will reduce the workload of green space statistics and achieve a high efficiency and accuracy for urban GSR surveys. This model would be applicable in the spatial mapping fields and the spatial planning fields.